Overview

Dataset statistics

Number of variables21
Number of observations41188
Missing cells0
Missing cells (%)0.0%
Duplicate rows12
Duplicate rows (%)< 0.1%
Total size in memory6.6 MiB
Average record size in memory168.0 B

Variable types

NUM10
CAT10
BOOL1

Warnings

Dataset has 12 (< 0.1%) duplicate rows Duplicates
euribor3m is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
emp.var.rate is highly correlated with euribor3m and 1 other fieldsHigh correlation
nr.employed is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
previous has 35563 (86.3%) zeros Zeros

Reproduction

Analysis started2021-09-13 20:09:03.295935
Analysis finished2021-09-13 20:09:31.351576
Duration28.06 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

age
Real number (ℝ≥0)

Distinct78
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.02406041
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Memory size321.8 KiB
2021-09-13T15:09:31.484732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.42124998
Coefficient of variation (CV)0.2603746315
Kurtosis0.7913115312
Mean40.02406041
Median Absolute Deviation (MAD)7
Skewness0.7846968158
Sum1648511
Variance108.6024512
MonotocityNot monotonic
2021-09-13T15:09:31.656503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3119474.7%
 
3218464.5%
 
3318334.5%
 
3617804.3%
 
3517594.3%
 
3417454.2%
 
3017144.2%
 
3714753.6%
 
2914533.5%
 
3914323.5%
 
Other values (68)2420458.8%
 
ValueCountFrequency (%) 
175< 0.1%
 
18280.1%
 
19420.1%
 
20650.2%
 
211020.2%
 
ValueCountFrequency (%) 
982< 0.1%
 
951< 0.1%
 
941< 0.1%
 
924< 0.1%
 
912< 0.1%
 

job
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
admin.
10422 
blue-collar
9254 
technician
6743 
services
3969 
management
2924 
Other values (7)
7876 
ValueCountFrequency (%) 
admin.1042225.3%
 
blue-collar925422.5%
 
technician674316.4%
 
services39699.6%
 
management29247.1%
 
retired17204.2%
 
entrepreneur14563.5%
 
self-employed14213.5%
 
housemaid10602.6%
 
unemployed10142.5%
 
Other values (2)12052.9%
 
2021-09-13T15:09:31.856265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-09-13T15:09:32.020019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length10
Mean length8.955229679
Min length6

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
married
24928 
single
11568 
divorced
4612 
unknown
 
80
ValueCountFrequency (%) 
married2492860.5%
 
single1156828.1%
 
divorced461211.2%
 
unknown800.2%
 
2021-09-13T15:09:32.187819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-09-13T15:09:32.291656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:32.454684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length7
Mean length6.831115859
Min length6

education
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
university.degree
12168 
high.school
9515 
basic.9y
6045 
professional.course
5243 
basic.4y
4176 
Other values (3)
4041 
ValueCountFrequency (%) 
university.degree1216829.5%
 
high.school951523.1%
 
basic.9y604514.7%
 
professional.course524312.7%
 
basic.4y417610.1%
 
basic.6y22925.6%
 
unknown17314.2%
 
illiterate18< 0.1%
 
2021-09-13T15:09:32.620543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-09-13T15:09:32.732424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:32.956095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length11
Mean length12.7109595
Min length7

default
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
no
32588 
unknown
8597 
yes
 
3
ValueCountFrequency (%) 
no3258879.1%
 
unknown859720.9%
 
yes3< 0.1%
 
2021-09-13T15:09:33.120050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-09-13T15:09:33.223912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:33.343752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length2
Mean length3.043702049
Min length2

housing
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
yes
21576 
no
18622 
unknown
 
990
ValueCountFrequency (%) 
yes2157652.4%
 
no1862245.2%
 
unknown9902.4%
 
2021-09-13T15:09:33.483566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-09-13T15:09:33.579415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:33.707245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length2.644022531
Min length2

loan
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
no
33950 
yes
6248 
unknown
 
990
ValueCountFrequency (%) 
no3395082.4%
 
yes624815.2%
 
unknown9902.4%
 
2021-09-13T15:09:34.018829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-09-13T15:09:34.134675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:34.262503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length2
Mean length2.271875303
Min length2

contact
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
cellular
26144 
telephone
15044 
ValueCountFrequency (%) 
cellular2614463.5%
 
telephone1504436.5%
 
2021-09-13T15:09:34.406312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-09-13T15:09:34.494194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:34.602050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length8
Mean length8.365252015
Min length8

month
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
may
13769 
jul
7174 
aug
6178 
jun
5318 
nov
4101 
Other values (5)
4648 
ValueCountFrequency (%) 
may1376933.4%
 
jul717417.4%
 
aug617815.0%
 
jun531812.9%
 
nov410110.0%
 
apr26326.4%
 
oct7181.7%
 
sep5701.4%
 
mar5461.3%
 
dec1820.4%
 
2021-09-13T15:09:34.753870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-09-13T15:09:34.873687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:35.112940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

day_of_week
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
thu
8623 
mon
8514 
wed
8134 
tue
8090 
fri
7827 
ValueCountFrequency (%) 
thu862320.9%
 
mon851420.7%
 
wed813419.7%
 
tue809019.6%
 
fri782719.0%
 
2021-09-13T15:09:35.268732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-09-13T15:09:35.388572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:35.544387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

duration
Real number (ℝ≥0)

Distinct1544
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.2850102
Minimum0
Maximum4918
Zeros4
Zeros (%)< 0.1%
Memory size321.8 KiB
2021-09-13T15:09:35.688172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1102
median180
Q3319
95-th percentile752.65
Maximum4918
Range4918
Interquartile range (IQR)217

Descriptive statistics

Standard deviation259.2792488
Coefficient of variation (CV)1.003849386
Kurtosis20.24793801
Mean258.2850102
Median Absolute Deviation (MAD)94
Skewness3.263141255
Sum10638243
Variance67225.72888
MonotocityNot monotonic
2021-09-13T15:09:35.855970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
851700.4%
 
901700.4%
 
1361680.4%
 
731670.4%
 
1241640.4%
 
871620.4%
 
1041610.4%
 
721610.4%
 
1111600.4%
 
1061590.4%
 
Other values (1534)3954696.0%
 
ValueCountFrequency (%) 
04< 0.1%
 
13< 0.1%
 
21< 0.1%
 
33< 0.1%
 
412< 0.1%
 
ValueCountFrequency (%) 
49181< 0.1%
 
41991< 0.1%
 
37851< 0.1%
 
36431< 0.1%
 
36311< 0.1%
 

campaign
Real number (ℝ≥0)

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.567592503
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Memory size321.8 KiB
2021-09-13T15:09:36.019752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.770013543
Coefficient of variation (CV)1.078836903
Kurtosis36.97979514
Mean2.567592503
Median Absolute Deviation (MAD)1
Skewness4.762506697
Sum105754
Variance7.672975028
MonotocityNot monotonic
2021-09-13T15:09:36.175522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%) 
11764242.8%
 
21057025.7%
 
3534113.0%
 
426516.4%
 
515993.9%
 
69792.4%
 
76291.5%
 
84001.0%
 
92830.7%
 
102250.5%
 
Other values (32)8692.1%
 
ValueCountFrequency (%) 
11764242.8%
 
21057025.7%
 
3534113.0%
 
426516.4%
 
515993.9%
 
ValueCountFrequency (%) 
561< 0.1%
 
432< 0.1%
 
422< 0.1%
 
411< 0.1%
 
402< 0.1%
 

pdays
Real number (ℝ≥0)

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean962.475454
Minimum0
Maximum999
Zeros15
Zeros (%)< 0.1%
Memory size321.8 KiB
2021-09-13T15:09:36.343298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186.9109073
Coefficient of variation (CV)0.194198103
Kurtosis22.22946263
Mean962.475454
Median Absolute Deviation (MAD)0
Skewness-4.922189916
Sum39642439
Variance34935.68728
MonotocityNot monotonic
2021-09-13T15:09:36.511074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%) 
9993967396.3%
 
34391.1%
 
64121.0%
 
41180.3%
 
9640.2%
 
2610.1%
 
7600.1%
 
12580.1%
 
10520.1%
 
5460.1%
 
Other values (17)2050.5%
 
ValueCountFrequency (%) 
015< 0.1%
 
1260.1%
 
2610.1%
 
34391.1%
 
41180.3%
 
ValueCountFrequency (%) 
9993967396.3%
 
271< 0.1%
 
261< 0.1%
 
251< 0.1%
 
223< 0.1%
 

previous
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1729629989
Minimum0
Maximum7
Zeros35563
Zeros (%)86.3%
Memory size321.8 KiB
2021-09-13T15:09:36.666867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4949010798
Coefficient of variation (CV)2.861311858
Kurtosis20.10881622
Mean0.1729629989
Median Absolute Deviation (MAD)0
Skewness3.832042243
Sum7124
Variance0.2449270788
MonotocityNot monotonic
2021-09-13T15:09:36.795491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
03556386.3%
 
1456111.1%
 
27541.8%
 
32160.5%
 
4700.2%
 
518< 0.1%
 
65< 0.1%
 
71< 0.1%
 
ValueCountFrequency (%) 
03556386.3%
 
1456111.1%
 
27541.8%
 
32160.5%
 
4700.2%
 
ValueCountFrequency (%) 
71< 0.1%
 
65< 0.1%
 
518< 0.1%
 
4700.2%
 
32160.5%
 

poutcome
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
nonexistent
35563 
failure
4252 
success
 
1373
ValueCountFrequency (%) 
nonexistent3556386.3%
 
failure425210.3%
 
success13733.3%
 
2021-09-13T15:09:36.964076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-09-13T15:09:37.074806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:37.210469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length10.45372439
Min length7

emp.var.rate
Real number (ℝ)

HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08188550063
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Memory size321.8 KiB
2021-09-13T15:09:37.340956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.570959741
Coefficient of variation (CV)19.18483405
Kurtosis-1.062631525
Mean0.08188550063
Median Absolute Deviation (MAD)0.3
Skewness-0.7240955492
Sum3372.7
Variance2.467914506
MonotocityNot monotonic
2021-09-13T15:09:37.452862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.41623439.4%
 
-1.8918422.3%
 
1.1776318.8%
 
-0.136838.9%
 
-2.916634.0%
 
-3.410712.6%
 
-1.77731.9%
 
-1.16351.5%
 
-31720.4%
 
-0.210< 0.1%
 
ValueCountFrequency (%) 
-3.410712.6%
 
-31720.4%
 
-2.916634.0%
 
-1.8918422.3%
 
-1.77731.9%
 
ValueCountFrequency (%) 
1.41623439.4%
 
1.1776318.8%
 
-0.136838.9%
 
-0.210< 0.1%
 
-1.16351.5%
 

cons.price.idx
Real number (ℝ≥0)

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.57566437
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Memory size321.8 KiB
2021-09-13T15:09:37.573416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.578840049
Coefficient of variation (CV)0.00618579684
Kurtosis-0.8298085772
Mean93.57566437
Median Absolute Deviation (MAD)0.38
Skewness-0.2308876514
Sum3854194.464
Variance0.3350558023
MonotocityNot monotonic
2021-09-13T15:09:37.721425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
93.994776318.8%
 
93.918668516.2%
 
92.893579414.1%
 
93.444517512.6%
 
94.465437410.6%
 
93.236168.8%
 
93.07524586.0%
 
92.2017701.9%
 
92.9637151.7%
 
92.4314471.1%
 
Other values (16)33918.2%
 
ValueCountFrequency (%) 
92.2017701.9%
 
92.3792670.6%
 
92.4314471.1%
 
92.4691780.4%
 
92.6493570.9%
 
ValueCountFrequency (%) 
94.7671280.3%
 
94.6012040.5%
 
94.465437410.6%
 
94.2153110.8%
 
94.1993030.7%
 

cons.conf.idx
Real number (ℝ)

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.50260027
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Memory size321.8 KiB
2021-09-13T15:09:37.861266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.628197856
Coefficient of variation (CV)-0.1142691537
Kurtosis-0.3585583105
Mean-40.50260027
Median Absolute Deviation (MAD)4.4
Skewness0.3031798587
Sum-1668221.1
Variance21.4202154
MonotocityNot monotonic
2021-09-13T15:09:37.996423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
-36.4776318.8%
 
-42.7668516.2%
 
-46.2579414.1%
 
-36.1517512.6%
 
-41.8437410.6%
 
-4236168.8%
 
-47.124586.0%
 
-31.47701.9%
 
-40.87151.7%
 
-26.94471.1%
 
Other values (16)33918.2%
 
ValueCountFrequency (%) 
-50.81280.3%
 
-502820.7%
 
-49.52040.5%
 
-47.124586.0%
 
-46.2579414.1%
 
ValueCountFrequency (%) 
-26.94471.1%
 
-29.82670.6%
 
-30.13570.9%
 
-31.47701.9%
 
-331720.4%
 

euribor3m
Real number (ℝ≥0)

HIGH CORRELATION

Distinct316
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.621290813
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Memory size321.8 KiB
2021-09-13T15:09:38.149663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.797
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.734447405
Coefficient of variation (CV)0.4789583313
Kurtosis-1.406802622
Mean3.621290813
Median Absolute Deviation (MAD)0.108
Skewness-0.7091879564
Sum149153.726
Variance3.0083078
MonotocityNot monotonic
2021-09-13T15:09:38.305455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4.85728687.0%
 
4.96226136.3%
 
4.96324876.0%
 
4.96119024.6%
 
4.85612102.9%
 
4.96411752.9%
 
1.40511692.8%
 
4.96510712.6%
 
4.86410442.5%
 
4.9610132.5%
 
Other values (306)2463659.8%
 
ValueCountFrequency (%) 
0.6348< 0.1%
 
0.635430.1%
 
0.63614< 0.1%
 
0.6376< 0.1%
 
0.6387< 0.1%
 
ValueCountFrequency (%) 
5.0459< 0.1%
 
57< 0.1%
 
4.971720.4%
 
4.9689922.4%
 
4.9676431.6%
 

nr.employed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167.035911
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Memory size321.8 KiB
2021-09-13T15:09:38.445268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5017.5
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.25152767
Coefficient of variation (CV)0.01398316732
Kurtosis-0.003760375696
Mean5167.035911
Median Absolute Deviation (MAD)37.1
Skewness-1.044262407
Sum212819875.1
Variance5220.28325
MonotocityNot monotonic
2021-09-13T15:09:38.569103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
5228.11623439.4%
 
5099.1853420.7%
 
5191776318.8%
 
5195.836838.9%
 
5076.216634.0%
 
5017.510712.6%
 
4991.67731.9%
 
5008.76501.6%
 
4963.66351.5%
 
5023.51720.4%
 
ValueCountFrequency (%) 
4963.66351.5%
 
4991.67731.9%
 
5008.76501.6%
 
5017.510712.6%
 
5023.51720.4%
 
ValueCountFrequency (%) 
5228.11623439.4%
 
5195.836838.9%
 
5191776318.8%
 
5176.310< 0.1%
 
5099.1853420.7%
 

y
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
no
36548 
yes
4640 
ValueCountFrequency (%) 
no3654888.7%
 
yes464011.3%
 
2021-09-13T15:09:38.664975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Interactions

2021-09-13T15:09:11.724895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:11.961240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:12.157714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:12.336236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:12.527750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:12.731182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:12.908706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:13.093213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:13.367480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:13.553981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:13.744472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:13.926022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:14.085582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:14.245168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:14.406701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:14.591231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:14.744797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:14.966206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:15.191603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:15.363145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:15.552637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:15.736183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:15.912703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:16.085213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:16.259777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:16.437304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:16.611830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:16.765432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:16.917025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:17.069616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:17.223206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:17.406681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:17.641055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:18.005088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:18.227486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:18.452883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:18.619465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:18.840846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:19.034328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:19.234794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:19.521028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:19.765374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:19.958857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:20.166303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:20.373747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:20.582191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:20.783659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:20.976137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:21.149673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:21.340163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:21.525668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:21.693255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:21.849837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:22.000434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:22.160048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:22.383410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:22.544979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:22.705551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:22.872135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:23.031713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:23.189284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:23.365785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:23.549293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:23.701885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:23.856507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:24.028013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:24.196564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:24.367107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:24.542638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:24.715176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:24.910654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:25.127074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:25.305598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:25.481129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:25.653667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:25.825209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:25.979796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:26.150374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:26.317891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:26.608144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:26.772676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:26.951232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:27.111769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:27.272340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:27.422938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:27.591512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:27.759039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:27.939555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:28.099128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:28.257704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:28.434304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:28.615786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:28.769375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:28.923962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:29.092545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:29.257072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:29.407703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:29.557490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:29.705258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:29.869088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-09-13T15:09:38.760847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-13T15:09:39.012535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-13T15:09:39.260180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-13T15:09:39.539807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-13T15:09:39.923309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-13T15:09:30.267677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-13T15:09:30.975460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
056housemaidmarriedbasic.4ynononotelephonemaymon26119990nonexistent1.193.994-36.44.8575191.0no
157servicesmarriedhigh.schoolunknownnonotelephonemaymon14919990nonexistent1.193.994-36.44.8575191.0no
237servicesmarriedhigh.schoolnoyesnotelephonemaymon22619990nonexistent1.193.994-36.44.8575191.0no
340admin.marriedbasic.6ynononotelephonemaymon15119990nonexistent1.193.994-36.44.8575191.0no
456servicesmarriedhigh.schoolnonoyestelephonemaymon30719990nonexistent1.193.994-36.44.8575191.0no
545servicesmarriedbasic.9yunknownnonotelephonemaymon19819990nonexistent1.193.994-36.44.8575191.0no
659admin.marriedprofessional.coursenononotelephonemaymon13919990nonexistent1.193.994-36.44.8575191.0no
741blue-collarmarriedunknownunknownnonotelephonemaymon21719990nonexistent1.193.994-36.44.8575191.0no
824techniciansingleprofessional.coursenoyesnotelephonemaymon38019990nonexistent1.193.994-36.44.8575191.0no
925servicessinglehigh.schoolnoyesnotelephonemaymon5019990nonexistent1.193.994-36.44.8575191.0no

Last rows

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
4117862retiredmarrieduniversity.degreenononocellularnovthu483263success-1.194.767-50.81.0314963.6yes
4117964retireddivorcedprofessional.coursenoyesnocellularnovfri15139990nonexistent-1.194.767-50.81.0284963.6no
4118036admin.marrieduniversity.degreenononocellularnovfri25429990nonexistent-1.194.767-50.81.0284963.6no
4118137admin.marrieduniversity.degreenoyesnocellularnovfri28119990nonexistent-1.194.767-50.81.0284963.6yes
4118229unemployedsinglebasic.4ynoyesnocellularnovfri112191success-1.194.767-50.81.0284963.6no
4118373retiredmarriedprofessional.coursenoyesnocellularnovfri33419990nonexistent-1.194.767-50.81.0284963.6yes
4118446blue-collarmarriedprofessional.coursenononocellularnovfri38319990nonexistent-1.194.767-50.81.0284963.6no
4118556retiredmarrieduniversity.degreenoyesnocellularnovfri18929990nonexistent-1.194.767-50.81.0284963.6no
4118644technicianmarriedprofessional.coursenononocellularnovfri44219990nonexistent-1.194.767-50.81.0284963.6yes
4118774retiredmarriedprofessional.coursenoyesnocellularnovfri23939991failure-1.194.767-50.81.0284963.6no

Duplicate rows

Most frequent

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedycount
024servicessinglehigh.schoolnoyesnocellularaprtue11419990nonexistent-1.893.075-47.11.4235099.1no2
127techniciansingleprofessional.coursenononocellularjulmon33129990nonexistent1.493.918-42.74.9625228.1no2
232techniciansingleprofessional.coursenoyesnocellularjulthu12819990nonexistent1.493.918-42.74.9685228.1no2
335admin.marrieduniversity.degreenoyesnocellularmayfri34849990nonexistent-1.892.893-46.21.3135099.1no2
436retiredmarriedunknownnononotelephonejulthu8819990nonexistent1.493.918-42.74.9665228.1no2
539admin.marrieduniversity.degreenononocellularnovtue12329990nonexistent-0.193.200-42.04.1535195.8no2
639blue-collarmarriedbasic.6ynononotelephonemaythu12419990nonexistent1.193.994-36.44.8555191.0no2
741technicianmarriedprofessional.coursenoyesnocellularaugtue12719990nonexistent1.493.444-36.14.9665228.1no2
845admin.marrieduniversity.degreenononocellularjulthu25219990nonexistent-2.992.469-33.61.0725076.2yes2
947techniciandivorcedhigh.schoolnoyesnocellularjulthu4339990nonexistent1.493.918-42.74.9625228.1no2